What is Spark’s checkpointing, and what is its role in a job?
Spark checkpoint is a mechanism that writes RDD data to disk during job execution, allowing for quick recovery in case of job failure.
The role of checkpoints in the job includes:
- Improve fault tolerance of jobs: By writing RDD data to disk, the amount of data that needs to be recalculated in case of job failure can be reduced, thereby enhancing the fault tolerance of the jobs.
- Speed up task execution: By reducing the amount of data that needs to be recalculated, the execution time of tasks can be reduced, ultimately speeding up task execution.
- Free up memory: In cases where memory is limited, RDD data can be written to disk using checkpoints to free up memory space and prevent OOM errors.
- Optimizing performance: adjusting the job execution by setting checkpoints can optimize job performance and improve efficiency.